Integrated Drill Boom Hole-Seeking Control via Reinforcement Learning (2312.01836v1)

Abstract: Intelligent drill boom hole-seeking is a promising technology for enhancing drilling efficiency, mitigating potential safety hazards, and relieving human operators. Most existing intelligent drill boom control methods rely on a hierarchical control framework based on inverse kinematics. However, these methods are generally time-consuming due to the computational complexity of inverse kinematics and the inefficiency of the sequential execution of multiple joints. To tackle these challenges, this study proposes an integrated drill boom control method based on Reinforcement Learning (RL). We develop an integrated drill boom control framework that utilizes a parameterized policy to directly generate control inputs for all joints at each time step, taking advantage of joint posture and target hole information. By formulating the hole-seeking task as a Markov decision process, contemporary mainstream RL algorithms can be directly employed to learn a hole-seeking policy, thus eliminating the need for inverse kinematics solutions and promoting cooperative multi-joint control. To enhance the drilling accuracy throughout the entire drilling process, we devise a state representation that combines Denavit-Hartenberg joint information and preview hole-seeking discrepancy data. Simulation results show that the proposed method significantly outperforms traditional methods in terms of hole-seeking accuracy and time efficiency.

• The paper introduces an RL-based integrated control framework that achieves a 5.7-fold efficiency improvement and maintains hole-seeking errors within 1cm precision.
• It employs a comprehensive MDP formulation by merging DH joint data with preview error information to enable simultaneous multi-joint control.
• Experimental evaluation using DSAC on MuJoCo simulations shows significant reductions in current and preview errors compared to traditional methods.

Integrated Drill Boom Hole-Seeking Control via Reinforcement Learning

The paper "Integrated Drill Boom Hole-Seeking Control via Reinforcement Learning" addresses the complexities and inefficiencies associated with traditional hierarchical control frameworks in intelligent drill boom operations. It proposes a novel integrated control methodology based on Reinforcement Learning (RL) to enhance drilling efficiency and accuracy.

The significance of drill boom automation in underground mining and tunneling cannot be understated, particularly in light of its potential to alleviate the labor-intensive, hazardous manual operations typically performed in these challenging environments. Traditional control frameworks, primarily based on inverse kinematics, typically exhibit two major drawbacks: high computational complexity and sequential control of multiple joints, which are time-consuming.

Methodology

The authors introduce an integrated control framework leveraging RL to generate direct control inputs for all joints simultaneously at each time step. This approach circumvents the computationally intensive inverse kinematics solutions and promotes cooperative multi-joint control. The hole-seeking task is modeled as a Markov Decision Process (MDP), allowing the use of contemporary RL algorithms to develop the control policy.

Key components of the MDP formulation include:

1. State Representation: The state representation merges the Denavit-Hartenberg (DH) parameterized joint posture information with preview hole-seeking discrepancy data. This state representation enhances performance by providing a comprehensive depiction of the current and anticipated errors between the drill boom and the target hole.
2. Action Space: The selected actions are the change rates of joint postures, ensuring smooth transitions and maintaining system stability.
3. Reward Function: The reward function penalizes current and preview discrepancies as well as the magnitude of the actions to ensure alignment precision and control smoothness.

Experimental Evaluation

The authors validated their proposed methodology through extensive simulations using the General Optimal control Problem Solver (GOPS) and environments built on the MuJoCo platform. Four RL algorithms suited for continuous control were employed: DSAC, SAC, TD3, and DDPG. Among these, DSAC demonstrated superior performance in terms of final accumulated rewards and hole-seeking precision.

From the numerical results presented, the integrated method showed a significant reduction in both current and preview hole-seeking errors compared to traditional methods. Specifically, the DSAC algorithm achieved errors well within 1cm, satisfying practical application requirements. Additionally, the integrated control framework outperformed the hierarchical approach in terms of control efficiency, demonstrating an approximately 5.7-fold improvement.

Implications and Future Work

The integrated control method presented not only improves hole-seeking accuracy but also significantly enhances drilling efficiency. This has substantial practical implications, potentially reducing the occurrence of issues such as overbreak and underbreak in tunneling operations. The elimination of inverse kinematics computation and the adoption of a coordinated multi-joint control strategy underscore the robustness and efficiency of the proposed methodology.

Future works should address state constraints for ensuring system safety during the hole-seeking process, further bolstering the practical applicability of this method. Additionally, exploring real-world deployment scenarios and integrating with more advanced RL techniques could provide further enhancements in performance and adaptability.

In summary, the research contributes significantly to the automation of drill boom operations by presenting a robust, time-efficient method that leverages cutting-edge RL techniques to overcome traditional challenges, positioning itself as a promising direction for future advancements in automated drilling and tunneling systems.